Software Platform for Analyzing Alzheimer's and Parkinson's fMRI Connectomes

用于分析阿尔茨海默病和帕金森病 fMRI 连接体的软件平台

• 批准号: 9139293
• 负责人: WILLIAM D. SHANNON
• 金额: $ 55.16万
• 依托单位: WILLIAM D SHANNON CONSULTING, LLC
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-15 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9139293
• 关键词: Accounting; Address; Algorithm Design; Algorithms; Alzheimer' s Disease; Area; BRAIN initiative; Biological Markers; Biomedical Research; Brain; Brain Diseases; Brain imaging; Brain region; Businesses; Cardiovascular Diseases; Clinic; Clinical Research; Clinical Sciences; Clinical Treatment; Complex; Computer software; Contracts; Data; Data Analyses; Data Analytics; Development; Diabetes Mellitus; Disease; Drug Targeting; Electroencephalography; Ensure; Experimental Designs; Feedback; Functional Magnetic Resonance Imaging; Funding; Future; Goals; Graph; Human; Imagery; Industry; Licensing; Long-Term Care; Marketing; Measures; Mental Health; Methods; Modeling; Neurosciences; Onset of illness; Outcome; Parkinson Disease; Patients; Pattern; Performance; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Positron-Emission Tomography; Prevalence; Prevention Research; Publishing; Research; Research Personnel; Science; Small Business Innovation Research Grant; Social Work; Software Design; Speed; Statistical Algorithm; Statistical Methods; Subgroup; Technology; Testing; Time; Translational Research; United States National Institutes of Health; Universities; Work; biomarker discovery; cloud based; commercial application; connectome; cost; disease diagnosis; improved; innovation; meetings; novel; novel therapeutics; open source; programs; public health relevance; real world application; research and development; single photon emission computed tomography; social; software as a service; tool; translational clinical trial; translational neuroscience; web based interface

 DESCRIPTION (provided by applicant): It is estimated that the cost in the US for brain disorders per year is $500 billion for treatment and long term care, with another $400 billion in other nonmedical costs. As a consequence, reducing the burden of human brain disorders is a key focus of the NIH BRAIN Initiative, with one strategy being the use of fMRI connectomes to study brain region functioning. Current approaches for analyzing connectomes use 'off-the-shelf' statistical methods not optimized for this type of data. In this Direct to Phase II SBIR we propose to further develop and strengthen our graphical object oriented data analysis software to solve data analysis, experimental design, and hypothesis testing problems in translational neuroscience. Product: This SBIR will produce analytical software for fMRI data in translational research. Users will access the software through a cloud-based Software-as-a-Service (SaaS) contract. The technical innovation of this SBIR statistical software designed specifically and optimized for fMRI brain imaging data is to support moving fMRI technology from basic R&D to translational clinical science. The impact of this technology are tools to improve brain imaging experimental design, improve biostatistical analysis of brain imaging data, and ultimately improve discovery of biomarkers and identify possible drug targets for clinical treatment. Long term goal: Extending our cloud-based Software-as-a-Service (SaaS) data analytics platform offers researchers an easy to use web-based interface for analyzing new and complex biomedical research data. As a platform technology, there are many applications in other areas of biomedical research that can be developed. Therefore, our business strategy is to identify the areas of science that have the greatest potential to make the move from the university research setting into the commercial arena, and that produce novel or complex data with no commercial analytics tools for translational clinical trials yet on the market. The Specific Aims of this SBIR Direct to Phase II project are: Aim 1: Develop statistical algorithms and software. Aim 2: Validate the methods. Aim 3: Apply the methods to real data. Expected outcomes: Validated statistical software proven to be more powerful than existing methods for analyzing fMRI connectome data in translational research.

 描述（由申请人提供）：据估计，美国每年用于治疗和长期护理的脑疾病费用为5000亿美元，另有4000亿美元用于其他非医疗费用。因此，减少人类大脑疾病的负担是NIH BRAIN Initiative的一个重点，其中一个策略是使用fMRI连接体来研究大脑区域功能。目前用于分析连接体的方法使用“现成的”统计方法，而不是针对这种类型的数据进行优化。在本直接至第二阶段SBIR中，我们建议 进一步开发和加强我们的图形对象导向数据分析软件，以解决转化神经科学中的数据分析、实验设计和假设检验问题。产品：该SBIR将为转化研究中的fMRI数据生成分析软件。用户将通过基于云的软件即服务（SaaS）合同访问该软件。这款SBIR统计软件的技术创新是专门为fMRI脑成像数据设计和优化的，它支持将fMRI技术从基础研发转移到转化临床科学。这项技术的影响是改善脑成像实验设计，改善脑成像数据的生物统计分析，并最终改善生物标志物的发现和确定临床治疗可能的药物靶点的工具。长期目标：扩展我们基于云的软件即服务（SaaS）数据分析平台，为研究人员提供易于使用的基于Web的界面，用于分析新的和复杂的生物医学研究数据。作为一种平台技术，在生物医学研究的其他领域有许多应用可以开发。因此，我们的商业战略是确定最有潜力从大学研究环境转向商业竞技场的科学领域，以及产生新颖或复杂数据的科学领域，而市场上尚未有用于转化临床试验的商业分析工具。本SBIR的具体目标 第二阶段项目的直接目标是：目标1：开发统计算法和软件。 目标2：改进方法。 目标3：将方法应用于真实的数据。预期成果：经过验证的统计软件被证明是更强大的功能磁共振成像连接体数据分析在翻译研究比现有的方法。